CRAN Task View: Robust Statistical Methods

Robust (or "resistant") methods for statistics modelling have been
available in S from the very beginning in the 1980s; and then in R in
package
stats.
Examples are
median(),
mean(*, trim =. ),
mad(),
IQR(),
or also
fivenum(), the statistic
behind
boxplot()
in package
graphics)
or
lowess()
(and
loess()) for robust
nonparametric regression, which had been complemented
by
runmed()
in 2003.
Much further important functionality has been made available in
recommended (and hence present in all R versions) package
MASS
(by Bill Venables and Brian Ripley, see
the
book
Modern Applied
Statistics with S
).
Most importantly, they provide
rlm()
for robust regression and
cov.rob()
for
robust multivariate scatter and covariance.

This task view is about R add-on packages providing newer or faster,
more efficient algorithms and notably for (robustification of) new models.

An international group of scientists working in the field of robust
statistics has made efforts (since October 2005) to coordinate several of
the scattered developments and make the important ones available
through a set of R packages complementing each other.
These should build on a basic package with "Essentials",
coined
robustbase
with (potentially many) other packages
building on top and extending the essential functionality to particular
models or applications.
Further, there is the quite comprehensive package
robust, a version of the robust library of S-PLUS,
as an R package now GPLicensed thanks to Insightful and Kjell Konis.
Originally, there has been much overlap between 'robustbase'
and 'robust', now
robust
depends
on
robustbase, the former providing convenient routines for
the casual user where the latter will contain the underlying
functionality, and provide the more advanced statistician with a
large range of options for robust modeling.

We structure the packages roughly into the following topics, and
typically will first mention functionality in packages
robustbase
and
robust.

The
RobPer
provides several methods for robust
periodogram estimation, notably for irregularly spaced time series.

Peter Ruckdeschel has started to lead an effort for a robust
time-series package, see
robust-ts
on R-Forge.

Further, robKalman,
"Routines for Robust Kalman
Filtering --- the ACM- and rLS-filter"
, is being developed, see
robkalman
on R-Forge.

Note however that these (last two items) are not yet available from CRAN.

Econometric Models
:
Econometricians tend to like HAC (heteroscedasticity and
autocorrelation corrected) standard errors. For a broad class of
models, these are provided by package
sandwich.
Note that
vcov(lmrob())
also uses a version of HAC
standard errors for its robustly estimated linear models.
See also the CRAN task view
Econometrics

robeth
contains R functions interfacing to the extensive
RobETH fortran library with many functions for regression,
multivariate estimation and more.

Other approaches to robust and resistant methodology
:

The package
distr
and its several child packages
also allow to explore robust estimation concepts, see e.g.,
distr
on R-Forge.

Notably, based on these,
the project
robast
aims for the implementation of R
packages for the computation of optimally robust estimators and
tests as well as the necessary infrastructure (mainly S4 classes
and methods) and diagnostics; cf. M. Kohl (2005).
It includes the R packages
RandVar,
RobAStBase,
RobLox,
RobLoxBioC,
RobRex.
Further,
ROptEst, and
ROptRegTS.